Optimization of solar-air source heat pump hybrid heating system

Abstract In order to construct a versatile, complementary, low-carbon, clean, and efficient heating system, this study explores the potential of a solar-air source heat pump (SAHP) hybrid heating system. A simulation model is established in TRNSYS, and the particle swarm algorithm (PSO) and genetic algorithm (GA) from GENOPT and MATLAB are employed. With the aim of minimizing the annual cost, optimization is conducted on the key parameters and operational strategies of the system. Comparative analysis between the two algorithms reveals that PSO slightly outperforms in cost reduction, while GA exhibits a slight advantage in enhancing system performance. Significantly improved energy savings are achieved by appropriately reducing the rated heating capacity of the air source heat pump and increasing the volume of the water tank.